Outcome Measures Per Block
2023-05-01
Total Time
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Total_Time 23 445. 161.
## 2 2 Total_Time 23 206. 86.3
## 3 3 Total_Time 23 152. 90.4Visualizing the data:
Looking at outliers and if they’re extreme:
## # A tibble: 4 × 14
## Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 BNC09 984. 35.8 948. 3088.
## 2 1 BNC28 189. 24.3 164. 1702.
## 3 1 BNC32 696. 37.6 658. 4336.
## 4 3 BNC09 381. 19.6 361. 2189.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## # Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>1 extreme outlier, but we will keep in the analysis for now.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Total_Time 0.866 0.00538
## 2 2 Total_Time 0.946 0.246
## 3 3 Total_Time 0.890 0.0160
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.27 27.85 79.626 1.61e-10 * 0.55## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Total… 1 2 23 23 7.95 22 6.49e- 8 1.95e-7 ****
## 2 Total… 1 3 23 23 10.5 22 4.92e-10 1.48e-9 ****
## 3 Total… 2 3 23 23 4.40 22 2.28e- 4 6.84e-4 ***Orientation Time
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Orientation_Time 23 40.1 14.5
## 2 2 Orientation_Time 23 24.9 7.09
## 3 3 Orientation_Time 23 20.1 7.38Visualizing the data:
Looking at outliers and if they’re extreme:
## # A tibble: 5 × 14
## Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 BNC07 436. 91.5 345. 2567.
## 2 2 BNC12 68.5 11.9 56.7 1209.
## 3 2 BNC30 134. 35.3 98.5 1394.
## 4 2 BNC32 338. 48.8 290. 2205.
## 5 3 BNC32 318. 42.7 275. 2431.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## # Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>2 extreme outliers, but we will keep in the analysis for now.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Orientation_Time 0.821 0.000856
## 2 2 Orientation_Time 0.863 0.00470
## 3 3 Orientation_Time 0.913 0.0480
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.1 24.11 29.353 9.09e-06 * 0.419## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Orient… 1 2 23 23 4.51 22 1.72e-4 5.16e-4 ***
## 2 Orient… 1 3 23 23 6.27 22 2.62e-6 7.86e-6 ****
## 3 Orient… 2 3 23 23 5.60 22 1.24e-5 3.72e-5 ****Navigation Time
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Navigation_Time 23 405. 160.
## 2 2 Navigation_Time 23 181. 82.9
## 3 3 Navigation_Time 23 132. 87.3Visualizing the data:
Looking at outliers and if they’re extreme:
## # A tibble: 3 × 14
## Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 BNC09 984. 35.8 948. 3088.
## 2 1 BNC32 696. 37.6 658. 4336.
## 3 3 BNC09 381. 19.6 361. 2189.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## # Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>1 extreme outliers, but we will keep in the analysis for now.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Navigation_Time 0.849 0.00256
## 2 2 Navigation_Time 0.948 0.264
## 3 3 Navigation_Time 0.881 0.0106
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.26 27.77 69.611 7.28e-10 * 0.525## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Naviga… 1 2 23 23 7.43 22 1.97e-7 5.91e-7 ****
## 2 Naviga… 1 3 23 23 9.85 22 1.58e-9 4.74e-9 ****
## 3 Naviga… 2 3 23 23 4.02 22 5.68e-4 2 e-3 **Distance
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Distance 23 2764. 818.
## 2 2 Distance 23 1778. 530.
## 3 3 Distance 23 1582. 565.Visualizing the data:
Looking at outliers and if they’re extreme:
## # A tibble: 1 × 14
## Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2 BNC08 272. 21.5 251. 3190.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## # Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>No extreme outliers.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Distance 0.931 0.116
## 2 2 Distance 0.884 0.0122
## 3 3 Distance 0.830 0.00120
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.57 34.5 37.526 1.74e-08 * 0.398## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Distan… 1 2 23 23 6.21 22 2.96e-6 8.88e-6 ****
## 2 Distan… 1 3 23 23 6.98 22 5.20e-7 1.56e-6 ****
## 3 Distan… 2 3 23 23 1.92 22 6.8 e-2 2.03e-1 nsSpeed
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Speed 23 7.43 1.87
## 2 2 Speed 23 10.7 3.22
## 3 3 Speed 23 13.0 3.80Visualizing the data:
Looking at outliers and if they’re extreme:
## # A tibble: 1 × 14
## Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2 BNC12 68.5 11.9 56.7 1209.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## # Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>No extreme outliers.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Speed 0.975 0.804
## 2 2 Speed 0.967 0.615
## 3 3 Speed 0.979 0.880
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.49 32.73 74.908 1.21e-11 * 0.366## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Speed 1 2 23 23 -8.29 22 3.28e-8 9.84e-8 ****
## 2 Speed 1 3 23 23 -9.67 22 2.21e-9 6.63e-9 ****
## 3 Speed 2 3 23 23 -6.11 22 3.76e-6 1.13e-5 ****Dwell Time
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Mean_Dwell 23 1.35 0.432
## 2 2 Mean_Dwell 23 0.906 0.287
## 3 3 Mean_Dwell 23 0.759 0.259Visualizing the data:
Looking at outliers and if they’re extreme:
## # A tibble: 4 × 14
## Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 BNC09 984. 35.8 948. 3088.
## 2 1 BNC35 550. 45.0 505. 1868.
## 3 2 BNC35 217. 27.1 190. 1234.
## 4 3 BNC35 189. 26.3 163. 1222.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## # Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>No extreme outliers.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Mean_Dwell 0.879 0.00968
## 2 2 Mean_Dwell 0.942 0.196
## 3 3 Mean_Dwell 0.903 0.0289
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.25 27.6 80.39 1.73e-10 * 0.369## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Mean_D… 1 2 23 23 9.14 22 6.02e-9 1.81e-8 ****
## 2 Mean_D… 1 3 23 23 9.43 22 3.46e-9 1.04e-8 ****
## 3 Mean_D… 2 3 23 23 5.27 22 2.74e-5 8.22e-5 ****Teleportations
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Teleportations 23 379. 138.
## 2 2 Teleportations 23 235. 92.5
## 3 3 Teleportations 23 206. 103.Visualizing the data:
Looking at outliers and if they’re extreme:
## # A tibble: 1 × 14
## Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2 BNC08 272. 21.5 251. 3190.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## # Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>No extreme outliers.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Teleportations 0.950 0.300
## 2 2 Teleportations 0.897 0.0218
## 3 3 Teleportations 0.834 0.00142
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.6 35.11 30.416 1.28e-07 * 0.319## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Telepo… 1 2 23 23 5.71 22 9.55e-6 2.86e-5 ****
## 2 Telepo… 1 3 23 23 6.25 22 2.76e-6 8.28e-6 ****
## 3 Telepo… 2 3 23 23 1.69 22 1.05e-1 3.15e-1 nsP Value Correction for Comparisons So Far
Variance X
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Var_X 23 9.37 3.18
## 2 2 Var_X 23 8.41 2.48
## 3 3 Var_X 23 8.05 2.27Visualizing the data:
Looking at outliers and if they’re extreme:
## [1] Block_num Participant Total_Time Orientation_Time
## [5] Navigation_Time Distance Speed Mean_Dwell
## [9] Var_X Var_Y Var_Z Teleportations
## [13] is.outlier is.extreme
## <0 rows> (or 0-length row.names)No extreme outliers.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Var_X 0.920 0.0680
## 2 2 Var_X 0.940 0.179
## 3 3 Var_X 0.976 0.832
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.47 32.37 7.236 0.005 * 0.043## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Var_X 1 2 23 23 3.00 22 0.007 0.02 *
## 2 Var_X 1 3 23 23 2.92 22 0.008 0.024 *
## 3 Var_X 2 3 23 23 1.30 22 0.208 0.624 nsVariance Y
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Var_Y 23 68.9 17.5
## 2 2 Var_Y 23 62.2 18.5
## 3 3 Var_Y 23 57.7 16.2Visualizing the data:
Looking at outliers and if they’re extreme:
## [1] Block_num Participant Total_Time Orientation_Time
## [5] Navigation_Time Distance Speed Mean_Dwell
## [9] Var_X Var_Y Var_Z Teleportations
## [13] is.outlier is.extreme
## <0 rows> (or 0-length row.names)No extreme outliers.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Var_Y 0.955 0.369
## 2 2 Var_Y 0.922 0.0744
## 3 3 Var_Y 0.956 0.388
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 2 44 4.35 0.019 * 0.067## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Var_Y 1 2 23 23 1.93 22 0.067 0.2 ns
## 2 Var_Y 1 3 23 23 2.72 22 0.012 0.037 *
## 3 Var_Y 2 3 23 23 1.17 22 0.256 0.768 nsVariance Z
Plot
Stats
Summary stats of the data:
## # A tibble: 3 × 5
## Block_num variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 1 Var_Z 23 5.08 1.30
## 2 2 Var_Z 23 4.64 0.951
## 3 3 Var_Z 23 4.56 1.10Visualizing the data:
Looking at outliers and if they’re extreme:
## # A tibble: 2 × 14
## Block_num Participant Total_Time Orientation_Time Navigation_Time Distance
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 3 BNC03 195. 24.1 170. 2243.
## 2 3 BNC33 73.1 25.0 48.0 1055.
## # ℹ 8 more variables: Speed <dbl>, Mean_Dwell <dbl>, Var_X <dbl>, Var_Y <dbl>,
## # Var_Z <dbl>, Teleportations <int>, is.outlier <lgl>, is.extreme <lgl>No extreme outliers.
Checking normality assumption:
## # A tibble: 3 × 4
## Block_num variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 1 Var_Z 0.956 0.380
## 2 2 Var_Z 0.884 0.0117
## 3 3 Var_Z 0.947 0.259
## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Block_num 1.49 32.85 4.088 0.036 * 0.042## # A tibble: 3 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Var_Z 1 2 23 23 3.09 22 0.005 0.016 *
## 2 Var_Z 1 3 23 23 2.14 22 0.044 0.132 ns
## 3 Var_Z 2 3 23 23 0.407 22 0.688 1 ns